Abstract

A fast algorithm is developed to implement a Bayesian beam-former that can estimate signals of unknown direction of arrival (DOA). In the Bayesian approach, the underlying DOA is assumed random and its a posteriori probability density function (PDF) is approximated by a discrete probability mass function. A Bayesian beamformer then balances a set of beamformers according to the associated weights. To obtain a close approximation of the a posteriori PDF, the number of samples must be sufficiently large, incurring a significant computational burden. In this paper, we exploit the structure of a uniform linear array (ULA) to show that samples of the a posteriori PDF can be computed efficiently using the fast Fourier transform (FFT). This leads to a fast algorithm for the Bayesian beamformer, which operates in O(MlogM + N2) operations where M is the number of samples and N is the number of sensors.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Pages413-416
Number of pages4
ISBN (Electronic)0780379977
DOIs
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2003-January

Other

OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
CountryUnited States
CitySt. Louis
Period9/28/0310/1/03

Keywords

  • Array signal processing
  • Bayesian methods
  • Computational efficiency
  • Direction of arrival estimation
  • Fast Fourier transforms
  • Frequency estimation
  • Probability density function
  • Radar
  • Sonar
  • Vectors

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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